Understanding the behavior of aggregate collections of individuals is a fascinating concern and a challenging problem. There is a wide variety of such collections. One of the most complex challenges is understanding the behavior of humans in groups including both organized (e.g., military units) and loosely coupled types (e.g., mobs, demonstrators). The study of aggregate behavior extends into areas such as a terrorist network organization, herding behavior, and other collections where an aggregate is composed of a large number of individuals.
Recent national and world events have markedly increased the demand for research in this area, and have widened the desired scope of such research. Group demonstrations at home and abroad continue to be a popular form of social expression. Rioting following sporting events or other triggering occurrences and requiring judicious use of force has become more commonplace. National defense organizations face new challenges including unconventional and asymmetric conflict and heterogeneous crowd activities in urban settings. This has resulted in a greater need for understanding and predicting aggregate behaviors.
Existing single-entity, monolithic models are characterized primarily by a constrained paradigm requiring complete knowledge of the behavior of the aggregate as a whole. This makes it difficult or impossible to effectively and accurately model behaviors frequently seen in real-world scenarios.
On the other hand, existing “agent-based” approaches uses complex models for each agent. These systems tend to be heavy consumers of system processing resources.